Performance Evaluation and Engineering Verification of Machine Learning Based Prediction Models for Slope Stability

Abstract

Stability evaluation of geotechnical engineering slopes is of great significance for the risk control and safe operation of many engineering. Machine learning methods can effectively establish the potential relationship between geological features and slope behavior under complex environments, to accurately evaluate the stability of slope rock and soil. This work investigated the performance of eight commonly used machine learning models to predict slope safety factors. First, the prediction system of slope safety factors based on machine learning was established by combining historical data of slopes for parameter optimization and cross-validation. Then, four accuracy evaluation indexes, MSE, RMSE, MAE, and Pearson correlation, were objectively weighted, and objective weighting-TOPSIS models were constructed to comprehensively quantify the performance of each model. Finally, the best machine learning model was used in the slope stability analysis of the Sino–Russian natural gas control section. The research results show that there are obvious differences in the prediction accuracy of the slope safety factor among different models. The ANN model has the highest evaluation accuracy, and the ensemble learning method performs well in the data set. The machine learning model can better predict the safety factor of the slope under different working conditions. The discrepancies with the numerical simulation results are related to the limitations of data sets and the differences in analysis methods. The analysis method of this study not only provides a new research idea and solution for the construction and evaluation of the model predicting slope safety factors, but also applies to other geotechnical engineering instability problems.